用振动发电的无线传感标签，靠光脉冲+神经网络识活动。
Vibe2Spike: Batteryless Wireless Tags for Vibration Sensing with Event Cameras and Spiking Networks
- 振动发电标签发出光脉冲，事件相机捕捉后用脉冲神经网络识别。
- 五类设备平均识别准确率94.9%，支持低延迟与高精度平衡。
- 无需电池和射频，适合大规模智能环境部署。
密集、低成本传感器的部署对实现无处不在的智能环境至关重要。然而，现有传感方案在电池维护、无线传输开销和数据处理复杂性带来的能量、可扩展性和可靠性权衡中面临挑战。本文提出Vibe2Spike，一种新型无电池、无线传感框架，利用可见光通信（VLC）和脉冲神经网络（SNNs）实现基于振动的活动识别。系统采用仅由压电圆盘、齐纳二极管和LED组成的超低成本标签，通过振动能量收集并发射稀疏的可见光脉冲，无需电池或射频无线电。这些光脉冲由事件相机捕获，并使用通过EONS框架优化的SNN模型进行分类。我们在五类设备上评估了Vibe2Spike，平均分类准确率达94.9%，同时分析了不同时间分箱策略的延迟-精度权衡。Vibe2Spike展示了可扩展、节能的无电池智能环境实现路径。
原文摘要 · Abstract (English)
The deployment of dense, low-cost sensors is critical for realizing ubiquitous smart environments. However, existing sensing solutions struggle with the energy, scalability, and reliability trade-offs imposed by battery maintenance, wireless transmission overhead, and data processing complexity. In this work, we present Vibe2Spike, a novel battery-free, wireless sensing framework that enables vibration-based activity recognition using visible light communication (VLC) and spiking neural networks (SNNs). Our system uses ultra-low-cost tags composed only of a piezoelectric disc, a Zener diode, and an LED, which harvest vibration energy and emit sparse visible light spikes without requiring batteries or RF radios. These optical spikes are captured by event cameras and classified using optimized SNN models evolved via the EONS framework. We evaluate Vibe2Spike across five device classes, achieving 94.9\% average classification fitness while analyzing the latency-accuracy trade-offs of different temporal binning strategies. Vibe2Spike demonstrates a scalable, and energy-efficient approach for enabling intelligent environments in a batteryless manner.
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